# Loading Libraries
library(tidyverse)
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## v tibble 3.1.0 v dplyr 1.0.5
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
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library(magrittr)
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## set_names
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## extract
library(modelr)
library(nycflights13)
library(lubridate)
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library(splines)
library(forcats)
library(plotly)
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The data is provided by data.gov.uk. It describes road accidents across Leeds in the year 2017. Between all datasets, this dataset was selected because it includes around 2,200 accident with 15 variable that can be easily parsed and explored. Also, we selected this dataset because we believe that there are different questions that can be asked about the data, and that the data can provide interesting answers for our questions. The dataset includes data such as location, date, time, road setting and casualty information for each accident that transpired during the year.
Reference : https://data.world/datagov-uk/6efe5505-941f-45bf-b576-4c1e09b579a1
# Loading & Parsing Data
Accidents_2017 <- read_csv("Data/datagov-uk-6efe5505-941f-45bf-b576-4c1e09b579a1/2017-8.csv",
col_names = c("Reference_Number","Easting","Northing","Vehicles_Num","Accident_Date","Time","Road_Class","Road_Surface",
"Lightning_Cond","Weather_Cond","Vehicle_Type","Casualty_Class","Severity","Gender","Age"), skip = 1)
##
## -- Column specification --------------------------------------------------------
## cols(
## Reference_Number = col_character(),
## Easting = col_double(),
## Northing = col_double(),
## Vehicles_Num = col_double(),
## Accident_Date = col_character(),
## Time = col_character(),
## Road_Class = col_character(),
## Road_Surface = col_character(),
## Lightning_Cond = col_character(),
## Weather_Cond = col_character(),
## Vehicle_Type = col_character(),
## Casualty_Class = col_character(),
## Severity = col_character(),
## Gender = col_character(),
## Age = col_double()
## )
Accidents_Date <- Accidents_2017$Accident_Date
Accidents_Date <- parse_date(Accidents_Date, "%m/%d/%Y")
Time <- Accidents_2017$Time
Time <- parse_time(Time, "%H%M")
Accidents_2017 <- Accidents_2017 %>%
select(1:4, 7:15) %>%
mutate(Accidents_Date, Time)
Accidents_2017 <- Accidents_2017[,c(1:4,14,15,5:13)]
# Correcting spelling mistakes
Lightning_Cond <-
str_replace(Accidents_2017$Lightning_Cond,"Darkness: Street lights present and lit and lit","Darkness: Street lights present and lit")
Road_Class <- str_replace_all(Accidents_2017$Road_Class,
c("A.*" = "A","B.*" = "B","M.*" = "M"))
Road_Surface <- str_replace(Accidents_2017$Road_Surface,"^F.*","Snow")
Weather_Cond <- word(Accidents_2017$Weather_Cond,1)
Weather_Cond <- str_replace(Weather_Cond,"Fog","Other")
Vehicle_Type <- word(Accidents_2017$Vehicle_Type,1)
Vehicle_Type <- str_replace_all(Vehicle_Type,
c(".Private" = "Taxi",
"Ca.*" = "Car","Pedal" = "Cycle"))
Vehicle_Type <- str_replace(Vehicle_Type,"TaxiTaxi", "Taxi")
# Adding the improved columns to the dataset
Accidents_2017 <- Accidents_2017 %>%
select(1:6,12:15) %>%
mutate(Lightning_Cond,Weather_Cond,Road_Class,Road_Surface,Vehicle_Type,
Year = year(Accidents_Date),
Month = month(Accidents_Date),
Day = day(Accidents_Date),
Hour = hour(Time),
Minute = minute(Time),
Accidents_DateTime = make_datetime(Year, Month, Day, Hour, Minute))
Accidents_2017 <- Accidents_2017[,c(1:4, 16:20, 5:6, 21, 7:15)]
# Assigning the columns to their proper classes.
for(i in 2:length(Accidents_2017)) {
if(is.character(Accidents_2017[[i]])) {
Accidents_2017[[i]] <- as.factor(Accidents_2017[[i]])
} else if (is.numeric(Accidents_2017[[i]])) {
Accidents_2017[[i]] <- as.numeric(Accidents_2017[[i]])
}
}
Accidents_2017
## # A tibble: 2,203 x 21
## Reference_Number Easting Northing Vehicles_Num Year Month Day Hour Minute
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3AP0313 426340 428455 1 2017 3 17 8 15
## 2 3BE0850 430828 433222 2 2017 1 14 13 30
## 3 4110858 428940 429856 2 2017 1 1 8 5
## 4 4110858 428940 429856 2 2017 1 1 8 5
## 5 4111495 429899 434277 2 2017 1 1 17 5
## 6 4111706 435946 436807 2 2017 1 1 12 0
## 7 4120471 443658 436768 3 2017 1 2 12 30
## 8 4120471 443658 436768 3 2017 1 2 12 30
## 9 4121054 442103 434572 2 2017 1 2 18 7
## 10 4121054 442103 434572 2 2017 1 2 18 7
## # ... with 2,193 more rows, and 12 more variables: Accidents_Date <date>,
## # Time <time>, Accidents_DateTime <dttm>, Casualty_Class <fct>,
## # Severity <fct>, Gender <fct>, Age <dbl>, Lightning_Cond <fct>,
## # Weather_Cond <fct>, Road_Class <fct>, Road_Surface <fct>,
## # Vehicle_Type <fct>
# Checking NAs
sum(rowSums(is.na(Accidents_2017)))
## [1] 0
# No NA values to explore
EDA1 <- Accidents_2017 %>%
group_by(Gender, Age) %>%
summarise(Count = n() ) %>%
ungroup() %>%
ggplot(mapping = aes(x=Age , y = Count)) +
geom_point(alpha = 0.6) +
facet_wrap(~Gender) +
ggtitle("Number of Casualties Per Age For Males and Females") +
ylab("Number of Casualties") +
theme_linedraw()
## `summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
layout_plot <- function(my_plot, x = -0.055, y = - 0.03){
my_plot[['x']][['layout']][['annotations']][[1]][['y']] <- x
my_plot[['x']][['layout']][['annotations']][[2]][['x']] <- y
my_plot
}
ggplotly(EDA1) %>% layout_plot
Number of Casualities and their Age. In addition, the plot is classified by the Gender, therefore; it shows how the relationship differs between males and females.Accidents_2017 %>%
group_by(Gender, Age) %>%
summarise(Count = n() )
## `summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
## # A tibble: 182 x 3
## # Groups: Gender [2]
## Gender Age Count
## <fct> <dbl> <int>
## 1 Female 1 8
## 2 Female 2 3
## 3 Female 3 6
## 4 Female 4 2
## 5 Female 5 6
## 6 Female 6 7
## 7 Female 7 8
## 8 Female 8 5
## 9 Female 9 4
## 10 Female 10 9
## # ... with 172 more rows
(Accidents_Hour <- Accidents_2017 %>% count(Hour))
## # A tibble: 24 x 2
## Hour n
## <dbl> <int>
## 1 0 30
## 2 1 23
## 3 2 9
## 4 3 15
## 5 4 16
## 6 5 12
## 7 6 41
## 8 7 85
## 9 8 143
## 10 9 105
## # ... with 14 more rows
(DoW_Hour <- Accidents_2017 %>%
mutate(day_of_week = wday(Accidents_Date, label = TRUE))%>%
count(day_of_week, Hour))
## # A tibble: 158 x 3
## day_of_week Hour n
## <ord> <dbl> <int>
## 1 Sun 0 14
## 2 Sun 1 6
## 3 Sun 2 2
## 4 Sun 3 2
## 5 Sun 4 6
## 6 Sun 6 5
## 7 Sun 7 1
## 8 Sun 8 8
## 9 Sun 9 6
## 10 Sun 10 5
## # ... with 148 more rows
ggplot(Accidents_Hour, mapping = aes(Hour, n)) +
geom_line() +
geom_point()
ggplot(DoW_Hour, mapping = aes(Hour, n, color = day_of_week))+
geom_line()+
geom_point()
ggplot(Accidents_Hour, mapping = aes(Hour,n))+
geom_line()+
geom_point()+
geom_line(data = DoW_Hour, mapping = aes(Hour,n,color = day_of_week))
# Distribution of Accidents across the course of the day for every day of the year
Accidents_2017 %>%
mutate(Accidents_by_Hour = update(Accidents_DateTime, yday = 1)) %>%
ggplot(mapping = aes(Accidents_by_Hour)) +
geom_freqpoly(binwidth = 3600) # 5 mins interval
# Distribution of Accidents across the course of the day for every day (and month) of the year
Accidents_2017 %>%
mutate(Accidents_by_Hour = update(Accidents_DateTime, yday = 1),
Accidents_by_Month = factor(Month)) %>%
ggplot(mapping = aes(Accidents_by_Hour, color = Accidents_by_Month)) +
geom_freqpoly(binwidth = 3600) # 1 hour interval
# Distribution of Accidents across the course of the day for every day (and month) of the year w/ y-axis as density
Accidents_2017 %>%
mutate(Accidents_by_Hour = update(Accidents_DateTime, yday = 1),
Accidents_by_Month = factor(Month)) %>%
ggplot(mapping = aes(Accidents_by_Hour, color = Accidents_by_Month)) +
geom_freqpoly(mapping = aes(y = ..density..), binwidth = 3600) # 1 hour interval
daily <- Accidents_2017 %>%
group_by(Accidents_Date) %>%
summarise(n = n()) %>%
mutate(wday = wday(Accidents_Date, label = T, abbr = F))
daily
## # A tibble: 360 x 3
## Accidents_Date n wday
## <date> <int> <ord>
## 1 2017-01-01 4 Sunday
## 2 2017-01-02 7 Monday
## 3 2017-01-03 3 Tuesday
## 4 2017-01-04 7 Wednesday
## 5 2017-01-05 4 Thursday
## 6 2017-01-06 5 Friday
## 7 2017-01-07 5 Saturday
## 8 2017-01-09 5 Monday
## 9 2017-01-10 9 Tuesday
## 10 2017-01-11 7 Wednesday
## # ... with 350 more rows
# Number of flights per day
ggplot(daily, mapping = aes(Accidents_Date, n)) +
geom_line()
# Number of flights in each day of the week
ggplot(daily, mapping = aes(wday, n)) +
geom_boxplot()
ggplot(daily, mapping = aes(wday, n)) +
geom_bar(stat = "identity")
# Fitting a linear model w/ predictions & residuals
model1 <- lm(n ~ wday, data = daily)
grid <- daily %>%
data_grid(wday) %>%
add_predictions(model1, "n")
ggplot(daily, mapping = aes(wday, n))+
geom_boxplot() +
geom_point(data = grid, colour = "red", size = 4)
daily <- daily %>%
add_residuals(model1)
ggplot(data = daily, mapping = aes(Accidents_Date, resid)) +
geom_ref_line(h = 0) +
geom_line()